Synthetic Biology & Bioinformatics Prospects in the Cancer Arena

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Synthetic Biology & Bioinformatics Prospects in the Cancer Arena

Lígia R. Rodrigues and Leon D. Kluskens IBB – Institute for Biotechnology and Bioengineering, Centre of Biological Engineering,

University of Minho, Campus de Gualtar, Braga Portugal

1. Introduction

Cancer is the second leading cause of mortality worldwide, with an expected 1.5-3.0 million

new cases and 0.5-2.0 million deaths in 2011 for the US and Europe, respectively (Jemal et

al., 2011). Hence, this is an enormously important health risk, and progress leading to

enhanced survival is a global priority. Strategies that have been pursued over the years

include the search for new biomarkers, drugs or treatments (Rodrigues et al., 2007).

Synthetic biology together with bioinformatics represents a powerful tool towards the

discovery of novel biomarkers and the design of new biosensors.

Traditionally, the majority of new drugs has been generated from compounds derived from

natural products (Neumann & Neumann-Staubitz, 2010). However, advances in genome

sequencing together with possible manipulation of biosynthetic pathways, constitute

important resources for screening and designing new drugs (Carothers et al. 2009).

Furthermore, the development of rational approaches through the use of bioinformatics for

data integration will enable the understanding of mechanisms underlying the anti-cancer

effect of such drugs (Leonard et al., 2008; Rocha et al., 2010).

Besides in biomarker development and the production of novel drugs, synthetic biology can

also play a crucial role in the level of specific drug targeting. Cells can be engineered to

recognize specific targets or conditions in our bodies that are not naturally recognized by

the immune system (Forbes, 2010).

Synthetic biology is the use of engineering principles to create, in a rational and systematic

way, functional systems based on the molecular machines and regulatory circuits of living

organisms or to re-design and fabricate existing biological systems (Benner & Sismour,

2005). The focus is often on ways of taking parts of natural biological systems, characterizing

and simplifying them, and using them as a component of a highly unnatural, engineered,

biological system (Endy, 2005). Virtually, through synthetic biology, solutions for the unmet

needs of humankind can be achieved, namely in the field of drug discovery. Indeed,

synthetic biology tools enable the elucidation of disease mechanisms, identification of

potential targets, discovery of new chemotherapeutics or design of novel drugs, as well as

the design of biological elements that recognize and target cancer cells. Furthermore,

through synthetic biology it is possible to develop economically attractive microbial

production processes for complex natural products.

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Computational Biology and Applied Bioinformatics 160

Bioinformatics is used in drug target identification and validation, and in the development

of biomarkers and tools to maximize the therapeutic benefit of drugs. Now that data on

cellular signalling pathways are available, integrated computational and experimental

projects are being developed, with the goal of enabling in silico pharmacology by linking the

genome, transcriptome and proteome to cellular pathophysiology. Furthermore,

sophisticated computational tools are being developed that enable the modelling and design

of new biological systems. A key component of any synthetic biology effort is the use of

quantitative models (Arkin, 2001). These models and their corresponding simulations enable

optimization of a system design, as well as guiding their subsequent analysis. Dynamic

models of gene regulatory and reaction networks are essential for the characterization of

artificial and synthetic systems (Rocha et al., 2008). Several software tools and standards

have been developed in order to facilitate model exchange and reuse (Rocha et al., 2010).

In this chapter, synthetic biology approaches for cancer diagnosis and drug development will be reviewed. Specifically, examples on the design of RNA-based biosensors, bacteria and virus as anti-cancer agents, and engineered microbial cell factories for the production of drugs, will be presented.

2. Synthetic biology: tools to design, build and optimize biological processes

Synthetic biology uses biological insights combined with engineering principles to design

and build new biological functions and complex artificial systems that do not occur in

Nature (Andrianantoandro et al., 2006). The building blocks used in synthetic biology are

the components of molecular biology processes: promoter sequences, operator sequences,

ribosome binding sites (RBS), termination sites, reporter proteins, and transcription factors.

Examples of such building blocks are given in Table 1.

Great developments of DNA synthesis technologies have opened new perspectives for the design of very large and complex circuits (Purnick & Weiss, 2009), making it now affordable to synthesize a given gene instead of cloning it. It is possible to synthesize de novo a small virus (Mueller et al., 2009), to replace the genome of one bacterium by another (Lartigue et al., 2007) and to make large chunks of DNA coding for elaborate genetic circuits. Software tools to simulate large networks and the entire panel of omics technologies to analyze the engineered microorganism are available (for details see section 3). Finally, the repositories of biological parts (e.g. Registry of Standard Biological Parts (http://partsregistry.org/)) will increase in complexity, number and reliability of circuits available for different species. Currently, the design and synthesis of biological systems are not decoupled. For example, the construction of metabolic pathways or any circuit from genetic parts first requires a collection of well characterized parts, which do not yet fully exist. Nevertheless, this limitation is being addressed through the development and compilation of standard biological parts (Kelly et al., 2009). When designing individual biological parts, the base-by-base content of that part (promoter, RBS, protein coding region, terminator, among others) is explicitly dictated (McArthur IV & Fong, 2010). Rules and guidelines for designing genetic parts at this level are being established (Canton et al., 2008). Particularly, an important issue when designing protein-coding parts is codon optimization, encoding the same amino acid sequence with an alternative, preferred nucleotide sequence. Although a particular sequence, when expressed, may be theoretically functional, its expression may be far from optimal or even completely suppressed due to codon usage bias in the heterologous host.

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Synthetic Biology & Bioinformatics Prospects in the Cancer Arena 161

Genetic part Examples Rationale

Transcriptional control

Constitutive promoters lacIq, SV40, T7, sp6 “Always on” transcription

Regulatory regions tetO, lacO, ara, gal4, rhl box Repressor and activator sites

Inducible promoters ara, ethanol, lac, gal, rhl, lux, fdhH, sal, glnK, cyc1

Control of the promoter by induction or by cell state

Cell fate regulators GATA factors Control cell differentiation

Transcriptional control

RNA interference (RNAi)

Logic functions, RNAi repressor

Genetic switch, logic evaluation and gene silencing

Riboregulators Ligand-controlled ribozymes

Switches for detection and actuation

Ribosome binding site Kozak consensus sequence mutants

Control the level of translation

Post-transcriptional control

Phosphorylation cascades

Yeast phosphorylation pathway

Modulate genetic circuit behavior

Protein receptor design TNT, ACT and EST receptors

Control detection thresholds and combinatorial protein function

Protein degradation Ssra tags, peptides rich in Pro, Glu, Ser and Thr

Protein degradation at varying rates

Localization signals Nuclear localization, nuclear export and mitochondrial localization signals

Import or export from nucleus and mitochondria

Others

Reporter genes GFP, YFP, CFP, LacZ Detection of expression

Antibiotic resistance ampicilin, chloramphenicol

Selection of cells

Table 1. Genetic elements used as components of synthetic regulatory networks (adapted from McArthur IV & Fong, 2010 and Purnick & Weiss, 2009). Legend: CFP, cyan fluorescent protein; GFP, green fluorescent protein; YFP, yellow fluorescent protein.

Codon optimization of coding sequences can be achieved using freely available algorithms

such as Gene Designer (see section 3). Besides codon optimization, compliance with

standard assembly requirements and part-specific objectives including activity or specificity

modifications should be considered. For example, the BioBrick methodology requires that

parts exclude four standard restriction enzyme sites, which are reserved for use in assembly

(Shetty et al., 2008). Extensive collections of parts can be generated by using a naturally

occurring part as a template and rationally modifying it to create a library of that particular

genetic part. Significant progress in this area has been recently demonstrated for promoters

and RBS (Ellis et al., 2009; Salis et al., 2009). Ellis and co-workers (2009) constructed two

promoter libraries that can be used to tune network behavior a priori by fitting mathematical

promoter models with measured parameters. By using this model-guided design approach

the authors were able to limit the variability of the system and increase predictability.

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However, it is well-known that noisy or leaky promoters can complicate the system design.

In these cases a finer control over expression can be established by weakening the binding

strength of the downstream gene (Ham et al., 2006), or by using two promoter inputs to

drive transcription of an output via a modular AND gate (Anderson et al., 2006).

Additionally, modular and scalable RNA-based devices (aptamers, ribozymes, and

transmitter sequences) can be engineered to regulate gene transcription or translation (Win

& Smolke, 2007).

Design at the pathway level is not only concerned with including the necessary parts, but

also with controlling the expressed functionality of those parts. Parts-based synthetic

metabolic pathways will require tunable control, just as their natural counterparts which

often employ feedback and feed-forward motifs to achieve complex regulation (Purnick &

Weiss, 2009). Using a synthetic biology approach, the design of DNA sequences encoding

metabolic pathways (e.g. operons) should be relatively straightforward. Synthetic scaffolds

and well-characterized families of regulatory parts have emerged as powerful tools for

engineering metabolism by providing rational methodologies for coordinating control of

multigene expression, as well as decoupling pathway design from construction (Ellis et al.,

2009). Pathway design should not overlook the fact that exogenous pathways interact with

native cellular components and have their own specific energy requirements. Therefore,

modifying endogenous gene expression may be necessary in addition to balancing cofactor

fluxes and installing membrane transporters (Park et al., 2008).

After designing parts, circuits or pathways, the genomic constructs ought to be manufactured through DNA synthesis. Nucleotide’s sequence information can be outsourced to synthesis companies (e.g. DNA2.0, GENEART or Genscript, among others). The convenience of this approach over traditional cloning allows for the systematic generation of genetic part variants such as promoter libraries. Also, it provides a way to eliminate restriction sites or undesirable RNA secondary structures, and to perform codon optimization. The ability to make large changes to DNA molecules has resulted in standardized methods for assembling basic genetic parts into larger composite devices, which facilitate part-sharing and faster system-level construction, as demonstrated by the BioBrick methodology (Shetty et al., 2008) and the Gateway cloning system (Hartley, 2003). Other approaches based on type II restriction enzymes, such as Golden Gate Shuffling, provide ways to assemble many more components together in one step (Engler et al., 2009). A similar one-step assembly approach, circular polymerase extension cloning (CPEC), avoids the need for restriction-ligation, or single-stranded homologous recombination altogether (Quan & Tian, 2009). Not only is this useful for cloning single genes, but also for assembling parts into larger sequences encoding entire metabolic pathways and for generating combinatorial part libraries. On a chromosomal level, disruption of genes in Escherichia coli and other microorganisms has become much faster with the development of RecBCD and lambda RED-assisted recombination systems (Datsenko & Wanner, 2000), allowing the insertion, deletion or modification by simply using linear gene fragments. Additionally, multiplex automated genome engineering (MAGE) has been introduced as another scalable, combinatorial method for producing large-scale genomic diversity (Wang et al., 2009). This approach makes chromosomal modification easier by simultaneously mutating target sites across the chromosome. Plasmid-based expression and chromosomal integration are the two common vehicles for implementing synthetic metabolic pathways. Recently, the chemically inducible chromosomal evolution (CIChE) was proposed as a long-

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term expression alternative method (Tyo et al., 2009). This new method avoids complications associated with plasmid replication and segregation, and can be used to integrate multiple copies of genes into the genome. All these techniques will provide technical platforms for the rapid synthesis of parts and subsequent pathways. The majority of the synthetic biology advances has been achieved purely in vitro (Isalan et al., 2008), or in microorganisms involving the design of small gene circuits without a direct practical application, although scientifically very exciting. These studies have offered fundamental insight into biological processes, like the role and sources of biological noise; the existence of biological modules with defined properties; the dynamics of oscillatory behavior; gene transcription and translation; or cell communication (Alon, 2003; Kobayashi et al., 2004). An interesting example of a larger system that has been redesigned is the refactoring of the T7 bacteriophage (Chan et al., 2005). Another successful example has been the production of terpenoid compounds in E. coli (Martin et al., 2003) and Saccharomyces cerevisiae (Ro et al., 2006) that can be used for the synthesis of artemisinin. Bacteria and fungi have long been used in numerous industrial microbiology applications, synthesizing important metabolites in large amounts. The production of amino acids, citric acid and enzymes are examples of other products of interest, overproduced by microorganisms. Genetic engineering of strains can contribute to the improvement of these production levels. Altogether, the ability to engineer biological systems will enable vast progress in existing applications and the development of several new possibilities. Furthermore, novel applications can be developed by coupling gene regulatory networks with biosensor modules and biological response systems. An extensive RNA-based framework has been developed for engineering ligand-controlled gene regulatory systems, called ribozyme switches. These switches exhibit tunable regulation, design modularity, and target specificity and could be used, for example, to regulate cell growth (Win & Smolke, 2007). Engineering interactions between programmed bacteria and mammalian cells will lead to exciting medical applications (Anderson et al., 2006). Synthetic biology will change the paradigm of the traditional approaches used to treat diseases by developing “smart” therapies where the therapeutic agent can perform computation and logic operations and make complex decisions (Andrianantoandro et al., 2006). There are also promising applications in the field of living vectors for gene therapy and chemical factories (Forbes, 2010; Leonard et al., 2008).

3. Bioinformatics: a rational path towards biological behavior predictability

In order to evolve as an engineering discipline, synthetic biology cannot rely on endless trial and error methods driven by verbal description of biomolecular interaction networks. Genome projects identify the components of gene networks in biological organisms, gene after gene, and DNA microarray experiments discover the network connections (Arkin, 2001). However, these data cannot adequately explain biomolecular phenomena or enable rational engineering of dynamic gene expression regulation. The challenge is then to reduce the amount and complexity of biological data into concise theoretical formulations with predictive ability, ultimately associating synthetic DNA sequences to dynamic phenotypes.

3.1 Models for synthetic biology

The engineering process usually involves multiple cycles of design, optimization and revision. This is particularly evident in the process of constructing gene circuits (Marguet et

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al., 2007). Due to the large number of participating species and the complexity of their interactions, it becomes difficult to intuitively predict a design behavior. Therefore, only detailed modeling can allow the investigation of dynamic gene expression in a way fit for analysis and design (Di Ventura et al., 2006). Modeling a cellular process can highlight which experiments are likely to be the most informative in testing model hypothesis, and for example allow testing for the effect of drugs (Di Bernardo et al., 2005) or mutant phenotypes (Segre et al., 2002) on cellular processes, thus paving the way for individualized medicine. Data are the precursor to any model, and the need to organize as much experimental data as

possible in a systematic manner has led to several excellent databases as summarized in

Table 2. The term “model” can be used for verbal or graphical descriptions of a mechanism

underlying a cellular process, or refer to a set of equations expressing in a formal and exact

manner the relationships among variables that characterize the state of a biological system

(Di Ventura et al., 2006). The importance of mathematical modeling has been extensively

demonstrated in systems biology (You, 2004), although its utility in synthetic biology seems

even more dominant (Kaznessis, 2009).

Name Website

BIND (Biomolecular Interaction Network Database) http://www.bind.ca/

Brenda (a comprehensive enzyme information system)

http://www.brenda.uni-koeln.de/

CSNDB (Cell Signaling Networks Database) http://geo.nihs.go.jp/csndb/

DIP (Database of Interacting Proteins) http://dip.doe-mbi.ucla.edu/

EcoCyc/Metacyc/BioCyc (Encyclopedia of E. coli genes and metabolism)

http://ecocyc.org/

EMP (Enzymes and Metabolic Pathways Database) http://www.empproject.com/

GeneNet (information on gene networks) http://wwwmgs.bionet.nsc.ru/mgs/systems/genenet/

Kegg (Kyoto Encyclopedia of Genes and Genomes) http://www.genome.ad.jp/kegg/kegg.html

SPAD (Signaling Pathway Database) http://www.grt.kyushu-u.ac.jp/eny-doc/

RegulonDB (E. coli K12 transcriptional network) http://regulondb.ccg.unam.mx/

ExPASy-beta (Bioinformatics Resource Portal) http://beta.expasy.org/

Table 2. Databases of molecular properties, interactions and pathways (adapted from Arkin, 2001).

Model-driven rational engineering of synthetic gene networks is possible at the level of topologies or at the level of molecular components. In the first one, it is considered that molecules control the concentration of other molecules, e.g. DNA-binding proteins regulate the expression of specific genes by either activation or repression. By combining simple regulatory interactions, such as negative and positive feedback and feed-forward loops, one may create more complex networks that precisely control the production of protein

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molecules (e.g. bistable switches, oscillators, and filters). Experimentally, these networks can be created using existing libraries of regulatory proteins and their corresponding operator sites. Examples of these models are the oscillator described by Gardner et al (2000) and repressilator by Elowitz and Leibler (2000). In the second level, the kinetics and strengths of molecular interactions within the system are described. By altering the characteristics of the components, such as DNA-binding proteins and their corresponding DNA sites, one can modify the system dynamics without modifying the network topology. Experimentally, the DNA sequences that yield the desired characteristics of each component can be engineered to achieve the desired protein-protein, protein-RNA, or protein-DNA binding constants and enzymatic activities. For example, Alon and co-workers (2003) showed how simple mutations on the DNA sequence of the lactose operon can result in widely different phenotypic behavior. Various mathematical formulations can be used to model gene circuits. At the population

level, gene circuits can be modeled using ordinary differential equations (ODEs). In an ODE

formulation, the dynamics of the interactions within the circuit are deterministic. That is, the

ODE formulation ignores the randomness intrinsic to cellular processes, and is convenient

for circuit designs that are thought to be less affected by noise or when the impact of noise is

irrelevant (Marguet et al., 2007). An ODE model facilitates further sophisticated analyses,

such as sensitivity analysis and bifurcation analysis. Such analyses are useful to determine

how quantitative or qualitative circuit behavior will be impacted by changes in circuit

parameters. For instance, in designing a bistable toggle switch, bifurcation analysis was

used to explore how qualitative features of the circuit may depend on reaction parameters

(Gardner et al., 2000). Results of the analysis were used to guide the choice of genetic

components (genes, promoters and RBS) and growth conditions to favor a successful

implementation of designed circuit function. However, in a single cell, the gene circuit’s

dynamics often involve small numbers of interacting molecules that will result in highly

noisy dynamics even for expression of a single gene. For many gene circuits, the impact of

such cellular noise may be critical and needs to be considered (Di Ventura et al., 2006). This

can be done using stochastic models (Arkin, 2001). Different rounds of simulation using a

stochastic model will lead to different results each time, which presumably reflect aspects of

noisy dynamics inside a cell. For synthetic biology applications, the key of such analysis is

not necessarily to accurately predict the exact noise level at each time point. This is not

possible even for the simplest circuits due to the “extrinsic” noise component for each circuit

(Elowitz et al., 2002). Rather, it is a way to determine to what extent the designed function

can be maintained and, given a certain level of uncertainty or randomness, to what extent

additional layers of control can minimize or exploit such variations. Independently of the

model that is used, these can be evolved in silico to optimize designs towards a given

function. As an example, genetic algorithms were used by Francois and Hakim (2004) to

design gene regulatory networks exhibiting oscillations.

In most attempts to engineer gene circuits, mathematical models are often purposefully simplified to accommodate available computational power and to capture the qualitative behavior of the underlying systems. Simplification is beneficial partially due to the limited quantitative characterization of circuit elements, and partially because simpler models may better reveal key design constraints. The limitation, however, is that a simplified model may fail to capture richer dynamics intrinsic to a circuit. Synthetic models combine features of mathematical models and model organisms. In the engineering of genetic networks,

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synthetic biologists start from mathematical models, which are used as the blueprints to engineer a model out of biological components that has the same materiality as model organism but is much less complex. The specific characteristics of synthetic models allow one to use them as tools in distinguishing between different mathematical models and evaluating results gained in performing experiments with model organisms (Loettgers, 2007).

3.2 Computational tools for synthetic biology

Computational tools are essential for synthetic biology to support the design procedure at

different levels. Due to the lack of quantitative characterizations of biological parts, most

design procedures are iterative requiring experimental validation to enable subsequent

refinements (Canton et al., 2008). Furthermore, stochastic noise, uncertainty about the

cellular environment of an engineered system, and little insulation of components

complicate the design process and require corresponding models and analysis methods (Di

Ventura et al., 2006). Many computational standards and tools developed in the field of

systems biology (Wierling et al., 2007) are applicable for synthetic biology as well.

As previously discussed, synthetic gene circuits can be constructed from a handful of basic

parts that can be described independently and assembled into interoperating modules of

different complexity. For this purpose, standardization and modularity of parts at different

levels is required (Canton et al., 2008). The Registry of Standard Biological Parts constitutes

a reference point for current research in synthetic biology and it provides relevant

information on several DNA-based synthetic or natural building blocks. Most

computational tools that specifically support the design of artificial gene circuits use

information from the abovementioned registry. Moreover, many of these tools share

standardized formats for the input/output files. The System Biology Markup Language

(SBML) (http://sbml.org) defines a widely accepted, XML-based format for the exchange of

mathematical models in biology. It provides a concise representation of the chemical

reactions embraced by a biological system. These can be translated into systems of ODEs or

into reaction systems amenable to stochastic simulations (Alon, 2003). Despite its large

applicability to simulations, SBML currently lacks modularity, which is not well aligned

with parts registries in synthetic biology. Alternatively, synthetic gene systems can be

described according to CellML language which is more modular (Cooling et al., 2008).

One important feature to enable the assembly of standard biological parts into gene circuits is that they share common inputs and outputs. Endy (2005) proposed RNA polymerases and ribosomes as the molecules that physically exchange information between parts. Their fluxes, measured in PoPS (Polymerase Per Second) and in RiPS (Ribosomes Per Second) represent biological currents (Canton et al., 2008). This picture, however, does not seem sufficient to describe all information exchanges even in simple engineered gene circuits, since other signal carriers like transcription factors and environmental “messages” should be explicitly introduced and not indirectly estimated by means of PoPS and RiPS (Marchisio & Stelling, 2008). Based on the assumption that parts share common input/output signals, several computational tools have been proposed for gene circuit design, as presented in Table 3. Comparing these circuit design tools it is obvious that we are still far from an ideal solution. The software tools differ in many aspects such as scope of parts and circuit descriptions, the mode of user interaction, and the integration with databases or other tools.

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Circuit design and simulation

Biojade http://web.mit.edu/jagoler/www/biojade/

Tinkercell http://www.tinkercell.com/Home

Asmparts http://soft.synth-bio.org/asmparts.html

ProMoT http://www.mpimagdeburg.mpg.de/projects/promot

GenoCAD http://www.genocad.org/genocad/

GEC http://research.microsoft.com/gec

TABASCO http://openwetware.org/wiki/TABASCO

Circuit optimization

Genetdes http://soft.synth-bio.org/genetdes.html

RoVerGeNe http://iasi.bu.edu/~batt/rovergene/rovergene.htm

DNA and RNA design

Gene Designer https://www.dna20.com/index.php?pageID=220

GeneDesign http://www.genedesign.org/

UNAFold http://www.bioinfo.rpi.edu/applications/hybrid/download.php

Vienna RNA package http://www.tbi.univie.ac.at/~ivo/RNA/

Zinc Finger Tools http://www.scripps.edu/mb/barbas/zfdesign/zfdesignhome.php

Protein Design

Rosetta http://www.rosettacommons.org/main.html

RAPTOR http://www.bioinformaticssolutions.com/products/raptor/index.php

PFP http://dragon.bio.purdue.edu/pfp/

Autodock 4.2 http://autodock.scripps.edu/

HEX 5.1 http://webloria.loria.fr/~ritchied/hex/

Integrated workflows

SynBioSS http://synbioss.sourceforge.net/

Clotho http://biocad-server.eecs.berkeley.edu/wiki/index.php/Tools

Biskit http://biskit.sf.net/

Table 3. Computational design tools for synthetic biology (adapted from Marchisio & Stelling, 2009; Matsuoka et al., 2009; and Prunick & Weiss, 2009)

Biojade was one of the first tools being reported for circuit design (Goler, 2004). It provides

connections to both parts databases and simulation environments, but it considers only one

kind of signal carrier (RNA polymerases). It can invoke the simulator TABASCO (Kosuri et

al., 2007), thus enabling genome scale simulations at single base-pair resolution.

CellDesigner (Funahashi et al., 2003) has similar capabilities for graphical circuit

composition. However, parts modularity and consequently circuit representation do not

appear detailed enough. Another tool for which parts communicate only by means of PoPS,

but not restricted to a single mathematical framework, is the Tinkercell. On the contrary, in

Asmparts (Rodrigo et al., 2007a) the circuit design is less straightforward and intuitive

because the tool lacks a Graphic User Interface. Nevertheless, each part exists as an

independent SBML module and the model kinetics for transcription and translation permit

to limit the number of parameters necessary for a qualitative system description. Marchisio

and Stelling (2008) developed a new framework for the design of synthetic circuits where

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each part is modeled independently following the ODE formalism. This results in a set of

composable parts that communicate by fluxes of signal carriers, whose overall amount is

constantly updated inside their corresponding pools. The model also considers transcription

factors, chemicals and small RNAs as signal carriers. Pools are placed among parts and

devices: they store free signal carriers and distribute them to the whole circuit. Hence,

polymerases and ribosomes have a finite amount; this permits to estimate circuit scalability

with respect to the number of parts. Mass action kinetics is fully employed and no

approximations are required to depict the interactions of signal carriers with DNA and

mRNA. The authors implemented the corresponding models into ProMoT (Process

Modeling Tool), software for the object-oriented and modular composition of models for

dynamic processes (Mirschel et al., 2009). GenoCAD (Czar et al., 2009) and GEC (Pedersen &

Phillips, 2009) introduce the notions of a grammar and of a programming language for

genetic circuit design, respectively. These tools use a set of rules to check the correct

composition of standard parts. Relying on libraries of standard parts that are not necessarily

taken from the Registry of Standard Biological Parts, these programs can translate a circuit

design into a complete DNA sequence. The two tools differ in capabilities and possible

connectivity to other tools.

The ultimate goal of designing a genetic circuit is that it works, i.e. that it performs a given

function. For that purpose, optimization cycles to establish an appropriate structure and a

good set of kinetic parameters values are required. These optimization problems are

extremely complex since they involve the selection of adequate parts and appropriate

continuous parameter values. Stochastic optimization methods (e.g. evolutionary

algorithms) attempt to find good solutions by biased random search. They have the

potential for finding globally optimal solutions, but optimization is computationally

expensive. On the other hand, deterministic methods (e.g. gradient descent) are local search

methods, with less computational cost, but at the expense of missing good solutions. The optimization problem can be tackled by tools such as Genetdes (Rodrigo et al., 2007b) and OptCircuit (Dasika & Maranas, 2008). They rely on different parts characterizations and optimization algorithms. Genetdes uses a stochastic method termed “Simulated Annealing” (Kirkpatrick et al., 1983), which produces a single solution starting from a random circuit configuration. As a drawback, the algorithm is more likely to get stuck in a local minimum than an evolutionary algorithm. OptCircuit, on the contrary, treats the circuit design problem with a deterministic method (Bansal et al., 2003), implementing a procedure towards a “local’ optimal solution. Each of these optimization algorithms requires a very simplified model for gene dynamics where, for instance, transcription and translation are treated as a single step process. Moreover, the current methods can cope only with rather small circuits. Another tool that has been described by Batt and co-workers (2007), RoVerGeNe, addresses the problem of parameter estimation more specifically. This tool permits to tune the performance and to estimate the robustness of a synthetic network with a known behavior and for which the topology does not require further improvement. Detailed design of synthetic parts that reproduce the estimated circuit kinetics and dynamics is a complex task. It requires computational tools in order to achieve error free solutions in a reasonable amount of time. Other than the placement/removal of restriction sites and the insertion/deletion of longer motifs, mutations of single nucleotides may be necessary to tune part characteristics (e.g. promoter strength and affinity toward regulatory factors). Gene Designer (Villalobos et al., 2006) is a complete tool for building artificial DNA

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segments and codon usage optimization. GeneDesign (Richardson et al., 2006) is another tool to design long synthetic DNA sequences. Many other tools are available for specific analysis of the DNA and RNA circuit components. The package UNAFold (Markham & Zuker, 2008) predicts the secondary structure of nucleic acid sequences to simulate their hybridizations and to estimate their melting temperature according to physical considerations. A more accurate analysis of the secondary structure of ribonucleic acids can be performed through the Vienna RNA package (Hofacker, 2003). Binding sites along a DNA chain can be located using Zinc Finger Tools (Mandell & Barbas, 2006). These tools allows one to search DNA sequences for target sites of particular zinc finger proteins (Kaiser, 2005), whose structure and composition can also be arranged. Thus, gene control by a class of proteins with either regulation or nuclease activity can be improved. Furthermore, tools that enable promoter predictions and primers design are available, such as BDGP and Primer3. Another relevant task in synthetic biology is the design and engineering of new proteins. Many tools have been proposed for structure prediction, homology modeling, function prediction, docking simulations and DNA-protein interactions evaluation. Examples include the Rosetta package (Simons et al., 1999); RAPTOR (Xu et al., 2003); PFP (Hawkins et al., 2006); Autodock 4.2 (Morris et al., 2009) and Hex 5.1 (Ritchie, 2008). Further advance in computational synthetic biology will result from tools that combine and integrate most of the tasks discussed, starting with the choice and assembly of biological parts to the compilation and modification of the corresponding DNA sequences. Examples of such tools comprise SynBioSS (Hill et al., 2008); Clotho and Biskit (Grunberg et al., 2007). Critical elements are still lacking, such as tools for automatic information integration (literature and databases), and tools that re-use standardized model entities for optimal circuit design. Overall, providing an extended and integrated information technology infrastructure will be crucial for the development of the synthetic biology field.

4. A roadmap from design to production of new drugs

Biological systems are dynamic, that is they mutate, evolve and are subject to noise. Currently, the full knowledge on how these systems work is still limited. As previously discussed, synthetic biology approaches involve breaking down organisms into a hierarchy of composable parts, which is useful for conceptualization purposes. Reprogramming a cell involves the creation of synthetic biological components by adding, removing, or changing genes and proteins. Nevertheless, it is important to notice that assembly of parts largely depends on the cellular context (the so-called chassis), thus restraining the abstraction of biological components into devices and modules, and their use in design and engineering of new organisms or functions. One level of abstraction from the DNA synthesis and manipulation is parts production, which optimization can be accomplished through either rational design or directed evolution. Applying rational design to parts alteration or creation is advantageous, in that it cannot only generate products with a defined function, but it can also produce biological insights into how the designed function comes about. However, it requires prior structural knowledge of the part, which is frequently unavailable. Directed evolution is an alternative method that can effectively address this limitation. Many synthetic biology applications will require parts for genetic circuits, cell–cell communication systems, and non-natural metabolic pathways that cannot be found in Nature, simply because Nature is not in need of them (Dougherty & Arnold, 2009). In essence, directed evolution begins with the generation

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of a library containing many different DNA molecules, often by error-prone DNA replication, DNA shuffling or combinatorial synthesis (Crameri et al., 1998). The library is next subjected to high-throughput screening or selection methods that maintain a link between genotype and phenotype in order to enrich the molecules that produce the desired function. Directed evolution can also be applied at other levels of biological hierarchy, for example to evolve entire gene circuits (Yokobayashi et al., 2002). Rational design and directed evolution should not be viewed as opposing methods, but as alternate ways to produce and optimize parts, each with their own unique strengths and weaknesses. Directed evolution can complement this technique, by using mutagenesis and subsequent screening for improved synthetic properties (Brustad & Arnold, 2010). In addition, methods have been developed to incorporate unnatural amino acids in peptides and proteins (Voloshchuk & Montclare, 2009). This will expand the toolbox of protein parts, and add beneficial effects, such as increased in vivo stability, when incorporated in proteinaceous therapeutics. Also, the development of de novo enzymes has seen a significant increase lately. The principle of computational design uses the design of a model, capable of stabilizing the transition state of a reaction. From there on, individual amino acids are positioned around it to create a catalytic site that stabilizes the transition state. The mRNA display technique resembles phage display and is a technique for the in vitro selection and evolution of proteins. Translated proteins are associated with their mRNA via a puromycin linkage. Selection occurs by binding to an immobilized substrate, after which a reverse transcriptase step will reveal the cDNA and thus the nucleotide sequence (Golynskiy & Seelig, 2010). If the selection step includes measurement of product formation from the substrate, novel peptides with catalytic properties can be selected. For the design, engineering, integration and testing of new synthetic gene networks, tools

and methods derived from experimental molecular biology must be used (for details see

section 2). Nevertheless, progress on these tools and methods is still not enough to

guarantee the complete success of the experiment. As a result, design of synthetic biological

systems has become an iterative process of modeling, construction, and experimental testing

that continues until a system achieves the desired behavior (Purnick & Weiss, 2009). The

process begins with the abstract design of devices, modules, or organisms, and is often

guided by mathematical models (Koide et al., 2009). Afterwards, the newly constructed

systems are tested experimentally. However, such initial attempts rarely yield fully

functional implementations due to incomplete biological information. Rational redesign

based on mathematical models improves system behavior in such situations (Koide et al.,

2009; Prather & Martin, 2008). Directed evolution is a complimentary approach, which can

yield novel and unexpected beneficial changes to the system (Yokobayashi et al., 2002).

These retooled systems are once again tested experimentally and the process is repeated as

needed. Many synthetic biological systems have been engineered successfully in this fashion

because the methodology is highly tolerant to uncertainty (Matsuoka et al., 2009). Figure 1

illustrates the above mentioned iterative approach used in synthetic biology.

Since its inception, metabolic engineering aims to optimize cellular metabolism for a

particular industrial process application through the use of directed genetic modifications

(Tyo et al., 2007). Metabolic engineering is often seen as a cyclic process (Nielsen, 2001),

where the cell factory is analyzed and an appropriate target is identified. This target is then

experimentally implemented and the resulting strain is characterized experimentally and, if

necessary, further analyses are conducted to identify novel targets. The application of

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synthetic biology to metabolic engineering can potentially create a paradigm shift. Rather

than starting with the full complement of components in a wild-type organism and

piecewise modifying and streamlining its function, metabolic engineering can be attempted

from a bottom-up, parts-based approach to design by carefully and rationally specifying the

inclusion of each necessary component (McArthur IV & Fong, 2010). The importance of

rationally designing improved or new microbial cell factories for the production of drugs

has grown substantially since there is an increasing need for new or existing drugs at prices

that can be affordable for low-income countries. Large-scale re-engineering of a biological

circuit will require systems-level optimization that will come from a deep understanding of

operational relationships among all the constituent parts of a cell. The integrated framework

necessary for conducting such complex bioengineering requires the convergence of systems

and synthetic biology (Koide et al., 2009). In recent years, with advances in systems biology

(Kitano, 2002), there has been an increasing trend toward using mathematical and

computational tools for the in silico design of enhanced microbial strains (Rocha et al., 2010).

Fig. 1. The iterative synthetic biology approach to design a given biological circuit/system.

Current models in both synthetic and systems biology emphasize the relationship between environmental influences and the responses of biological networks. Nevertheless, these models operate at different scales, and to understand the new paradigm of rational systems re-engineering, synthetic and systems biology fields must join forces (Koide et al., 2009). Synthetic biology and bottom-up systems biology methods extract discrete, accurate, quantitative, kinetic and mechanistic details of regulatory sub-circuits. The models generated from these approaches provide an explicit mathematical foundation that can ultimately be used in systems redesign and re-engineering. However, these approaches are confounded by high dimensionality, non-linearity and poor prior knowledge of key dynamic parameters (Fisher & Henzinger, 2007) when scaled to large systems.

MODEL FORMULATION

FROM MASS ACTION

KINETICS

SIMULATION

DYNAMIC PROPERTIES

EXPLOITATION

CIRCUIT CONSTRUCTION

EXPERIMENTAL

VALIDATION

CIRCUITS DEFINITION OR

IMPROVEMENT

CHARACTERIZATION

Kinetic model (ODE/SDE)

(experimental data; existing model

parameters)

In silico testing

(parameter space; dynamics)

Model organism (chassis)

�Platform (microscopy; flow cytometry; microfluidics)

�Read-out (phenotype; morphology; expression)

�Analysis (equilibrium; cellular context; genetic

background; environment)

�Circuit construction

re-engineer

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Consequently, modular sub-network characterization is performed assuming that the network is isolated from the rest of the host system. The top-down systems biology approach is based on data from high-throughput experiments that list the complete set of components within a system in a qualitative or semi-quantitative manner. Models of overall systems are similarly qualitative, tending toward algorithmic descriptions of component interactions. Such models are amenable to the experimental data used to develop them, but usually sacrifice the finer kinetic and mechanistic details of the molecular components involved (Price & Shmulevich, 2007). Bridging systems and synthetic biology approaches is being actively discussed and several solutions have been suggested (Koide et al., 2009). A typical synthetic biology project is the design and engineering of a new biosynthetic pathway in a model organism (chassis). Generally, E. coli is the preferred chassis since it is well-studied, easy to manipulate, and its reproduction in biological cultures is very handy. Initially, relevant databases like Kegg and BioCyc (Table 2) can be consulted for identifying all the possible metabolic routes that allow the production of a given drug from metabolites that exist in native E. coli. Then, for each reaction, the species that are known to possess the corresponding enzymes/genes must be identified. This step is relevant, since most often the same enzyme exhibits different kinetic behavior among different species. Information on sequences and kinetic parameters can be extracted from the above-mentioned sources, relevant literature and also from Brenda and Expasy databases. Afterwards, the information collected is used to build a family of dynamic models (Rocha et al., 2008, 2010) that enable the simulation of possible combinations regarding pathway configuration and the origin of the enzymes (associated with varying kinetic parameters). Optflux tool can be used for simulations and metabolic engineering purposes (http://www.optflux.org/). Using the same input (a fixed amount of precursor) it is possible to select the configuration that allows obtaining higher drug yields. Furthermore, through the use of genome-scale stoichiometric models coupled with the dynamic model it is possible to understand the likely limitations regarding the availability of the possible precursors. In fact, if the precursor for the given drug biosynthesis is a metabolic intermediate, possible limitations in its availability need to be addressed in order to devise strategies to cope with it. Based on this information, the next step involves the construction of the enzymatic reactions that will lead to the production of the drug from a metabolic precursor in E. coli. The required enzymes are then synthesized based on the gene sequences previously selected from the databases. The cloning strategy may include using a single plasmid with two different promoters; using two different plasmids, with different copy numbers and/or origins of replication; or ultimately integrating it into the genome, in order to allow fine tuning of the expression of the various enzymes necessary. Finally, a set of experiments using the engineered bacterium needs to be performed to evaluate its functionality, side-product formation and/or accumulation, production of intermediate metabolites and final product (desired drug). In the case of the previously mentioned artemisinin production, DNA microarray analysis and targeted metabolic profiling were used to optimize the synthetic pathway, reducing the accumulation of toxic intermediates (Kizer et al., 2008). These types of methodologies enable the validation of the drug production model and the design of strategies to further improve its production.

5. Novel strategies for cancer diagnosis and drug development

Cancer is a main issue for the modern society and according to the World Health Organization it is within the top 10 of leading causes of death in middle- and high-income

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countries. Several possibilities to further improve existing therapies and diagnostics, or to develop novel alternatives that still have not been foreseen, can be drawn using synthetic biology approaches. Promising future applications include the development of RNA-based biosensors to produce a desired response in vivo or to be integrated in a cancer diagnosis device; the design and engineering of bacteria that can be programmed to target a tumor and release a therapeutic agent in situ; the use of virus as a tool for recognizing tumors or for gene therapy; and the large scale production of complex chemotherapeutic agents, among others.

5.1 RNA-based biosensors

Synthetic biology seeks for new biological devices and systems that regulate gene

expression and metabolite pathways. Many components of a living cell possess the ability to

carry genetic information, such as DNA, RNA, proteins, among others. RNA has a critical

role in several functions (genetic translation, protein synthesis, signal recognition of

particles) due to its functional versatility from genetic blueprint (e.g. mRNA, RNA virus

genomes. Its catalytic function as enzyme (e.g. ribozymes, rRNA) and regulator of gene

expression (e.g. miRNA, siRNA) makes it stand out among other biopolymers with a more

specialized scope (e.g. DNA, proteins) (Dawid et al., 2009). Therefore, non-coding RNA

molecules enable the formation of complex structures that can interact with DNA, other

RNA molecules, proteins and other small molecules (Isaacs et al., 2006).

Natural biological systems contain transcription factors and regulators, as well as several

RNA-based mechanisms for regulating gene expression (Saito & Inoue, 2009). A number of

studies have been conducted on the use of RNA components in the construction of synthetic

biologic devices (Topp & Gallivan, 2007; Win & Smolke, 2007). The interaction of RNA with

proteins, metabolites and other nucleic acids is affected by the relationship between

sequence, structure and function. This is what makes the RNA molecule so attractive and

malleable to engineering complex and programmable functions.

5.1.1 Riboswitches

One of the most promising elements are the riboswitches, genetic control elements that allow small molecules to regulate gene expression. They are structured elements typically found in the 5’-untranslated regions of mRNA that recognize small molecules and respond by altering their three-dimensional structure. This, in turn, affects transcription elongation, translation initiation, or other steps of the process that lead to protein production (Beisel & Smolke, 2009; Winkler & Breaker, 2005). Biological cells can modulate gene expression in response to physical and chemical variations in the environment allowing them to control their metabolism and preventing the waste of energy expenditure or inappropriate physiological responses (Garst & Batey, 2009). There are currently at least twenty classes of riboswitches that recognize a wide range of ligands, including purine nucleobases (purine riboswitch), amino acids (lysine riboswitch), vitamin cofactors (cobalamin riboswitch), amino sugars, metal ions (mgtA riboswitch) and second messenger molecules (cyclic di-GMP riboswitch) (Beisel & Smolke, 2009). Riboswitches are typically composed of two distinct domains: a metabolite receptor known as the aptamer domain, and an expression platform whose secondary structure signals the regulatory response. Embedded within the aptamer domain is the switching sequence, a sequence shared between the aptamer domain and the expression platform (Garst & Batey, 2009). The aptamer domain is part of the RNA

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and forms precise three-dimensional structures. It is considered a structured nucleotide pocket belonging to the riboswitch, in the 5´-UTR, which when bound regulates downstream gene expression (Isaacs et al., 2006). Aptamers specifically recognize their corresponding target molecule, the ligand, within the complex group of other metabolites, with the appropriate affinity, such as dyes, biomarkers, proteins, peptides, aromatic small molecules, antibiotics and other biomolecules. Both the nucleotide sequence and the secondary structure of each aptamer remain highly conserved (Winkler & Breaker, 2005). Therefore, aptamer domains are the operators of the riboswitches. A strategy for finding new aptamer sequences is the use of SELEX (Systemic Evolution of

Ligands by Exponential enrichment method). SELEX is a combinatorial chemistry technique

for producing oligonucleotides of either single-stranded DNA or RNA that specifically bind

to one or more target ligands (Stoltenburg et al., 2007). The process begins with the synthesis

of a very large oligonucleotide library consisting of randomly generated sequences of fixed

length flanked by constant 5' and 3' ends that serve as primers. The sequences in the library

are exposed to the target ligand and those that do not bind the target are removed, usually

by affinity chromatography. The bound sequences are eluted and amplified by PCR to

prepare for subsequent rounds of selection in which the stringency of the elution conditions

is increased to identify the tightest-binding sequences (Stoltenburg et al., 2007). SELEX has

been used to evolve aptamers of extremely high binding affinity to a variety of target

ligands. Clinical uses of the technique are suggested by aptamers that bind tumor markers

(Ferreira et al., 2006). The aptamer sequence must then be placed near to the RBS of the

reporter gene, and inserted into E. coli (chassis), using a DNA carrier (i.e. plasmid), in order

to exert its regulatory function.

Synthetic riboswitches represent a powerful tool for the design of biological sensors that

can, for example, detect cancer cells, or the microenvironment of a tumor, and in the

presence of a given molecule perform a desired function, like the expression in situ of a

therapeutic agent. Several cancer biomarkers have been identified in the last decade;

therefore there are many opportunities of taking these compounds as templates to design

adequate riboswitches for their recognition. Alternatively, the engineering goal might be the

detection of some of these biomarkers in biological samples using biosensors with aptamers

as the biological recognition element, hence making it a less invasive approach. The

development of aptamer-based electrochemical biosensors has made the detection of small

and macromolecular analytes easier, faster, and more suited for early detection of protein

biomarkers (Hianik & Wang, 2009). Multi-sensor arrays that provide global information on

complex samples (e.g. biological samples) have deserved much interest recently. Coupling

an aptamer to these devices will increase its specificity and selectivity towards the selected

target(s). The selected target may be any serum biomarker that when detected in high

amounts in biological samples can be suggestive of tumor activity.

5.2 Bacteria as anti-cancer agents

Bacteria possess unique features that make them powerful candidates for treating cancer in ways that are unattainable by conventional methods. The moderate success of conventional methods, such as chemotherapy and radiation, is related to its toxicity to normal tissue and inability to destroy all cancer cells. Many bacteria have been reported to specifically target tumors, actively penetrate tissue, be easily detected and/or induce a controlled cytotoxicity. The possibility of engineering interactions between programmed bacteria and mammalian

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cells opens unforeseen progresses in the medical field. Emerging applications include the design of bacteria to produce therapeutic agents (in vitro or in vivo) and the use of live bacteria as targeted delivery systems (Forbes, 2010; Pawelek et al., 2003). An impressive example of these applications is described by Anderson and co-workers (2006). The authors have successfully engineered E. coli harboring designed plasmids to invade cancer cells in an environmentally controlled way, namely in a density-dependent manner under anaerobic growth conditions and arabinose induction. Plasmids were built containing the inv gene from Yersinia pseudotuberculosis under control of the Lux promoter, the hypoxia-responsive fdhF promoter, and the arabinose-inducible araBAD promoter. This is significant because the tumor environment is often hypoxic and allows for high bacterial cell densities due to depressed immune function in the tumor. Therefore, this work demonstrated, as a “proof of concept”, that one can potentially use engineered bacteria to target diseased cells without significantly impacting healthy cells. Ideally, an engineered bacterium for cancer therapy would specifically target tumors

enabling the use of more toxic molecules without systemic effects; be self-propelled enabling

its penetration into tumor regions that are inaccessible to passive therapies; be responsive to

external signals enabling the precise control of location and timing of cytotoxicity; be able to

sense the local environment allowing the development of responsive therapies that can

make decisions about where and when drugs are administered; and be externally detectable,

thus providing information about the state of the tumor, the success of localization and the

efficacy of treatment (Forbes, 2010). Indeed some of these features naturally exist in some

bacteria, e.g. many genera of bacteria have been shown to preferentially accumulate in

tumors, including Salmonella, Escherichia, Clostridium and Bifidobacterium. Moreover, bacteria

have motility (flagella) that enable tissue penetration and chemotactic receptors that direct

chemotaxis towards molecular signals in the tumor microenvironment. Selective

cytotoxicity can be engineered by transfection with genes for therapeutic molecules,

including toxins, cytokines, tumor antigens and apoptosis-inducing factors. External control

can be achieved using gene promoter strategies that respond to small molecules, heat or

radiation. Bacteria can be detected using light, magnetic resonance imaging and positron

emission tomography. At last, genetic manipulation of bacteria is easy, thus enabling the

development of treatment strategies, such as expression of anti-tumor proteins and

including vectors to infect cancer cells (Pawelek et al., 2003). To date, many different

bacterial strategies have been implemented in animal models (e.g. Salmonella has been tested

for breast, colon, hepatocellular, melanoma, neuroblastoma, pancreatic and prostate cancer),

and also some human trials (e.g. C. butyricum M-55 has been tested for squamous cell

carcinoma, metastic, malignant neuroma and melanoma) have been carried out (Forbes,

2010).

Ultrasound is one of the techniques often used to treat solid tumors (e.g. breast cancer);

however, this technique is not always successful, as sometimes it just heats the tumor

without destroying it. Therefore, we are currently engineering the heat shock response

machinery from E. coli to trigger the release of a therapeutic agent in situ concurrent with

ultrasound treatment. For that purpose, several modeling and engineering steps are being

implemented. The strategy being pursued is particularly useful for drugs that require in situ

synthesis because of a poor bioavailability, thereby avoiding repetitive oral doses to achieve

sufficient concentration inside the cells. The use of live bacteria for therapeutic purposes

naturally poses some issues (Pawelek et al., 2003), but currently the goal is to achieve the

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proof-of-concept that an engineered system will enable the production of a cancer-fighting

drug triggered by a temperature increase.

5.3 Alternative nanosized drug carriers

The design of novel tumor targeted multifunctional particles is another extremely interesting and innovative approach that makes use of the synthetic biology principles. The modest success of the traditional strategies for cancer treatment has driven research towards the development of new approaches underpinned by mechanistic understanding of cancer progression and targeted delivery of rational combination therapy.

5.3.1 Viral drug delivery systems

The use of viruses, in the form of vaccines, has been common practice ever since its first use

to combat smallpox. Recently, genetic engineering has enlarged the applications of viruses,

since it allows the removal of pathogen genes encoding virulence factors that are present in

the virus coat. As a result, it can elicit immunity without causing serious health effects in

humans. In the light of gene therapy, the use of virus-based entities hold a promising future,

since by nature, they are being delivered to human target cells, and can be easily

manipulated genetically. As such, they may be applied to target and lyse specific cancer

cells, delivering therapeutics in situ. Bacteriophages are viruses that specifically and only

infect bacteria. They have gained more attention the last decades, mainly in phage display

technology. In anti-cancer therapy, this technique has contributed enormously to the

identification of new tumor-targeting molecules (Brown, 2010). In vivo phage display

technology identified a peptide exhibiting high affinity to hepatocellular carcinoma cells

(Du et al., 2010). In a different approach, a phage display-selected ligand targeting breast

cancer cells was incorporated in liposomes containing siRNA. The delivered liposomes were

shown to significantly downregulate the PRDM14 gene in the MCF7 target cells (Bedi et al.,

2011). In addition, the option to directly use bacteriophages as drug-delivery platforms has

been explored. A recent study described the use of genetically modified phages able to

target tumor cell receptors via specific antibodies resulting in endocytosis, intracellular

degradation, and drug release (Bar et al., 2008). Using phage display a variety of cancer cell

binding and internalizing ligands have been selected (Gao et al., 2003). Bacteriophages can

also be applied to establish an immune response. Eriksson and co-workers (2009) showed

that a tumor-specific M13 bacteriophage induced regression of melanoma target cells,

involving tumor-associated macrophages and being Toll-like receptor-dependent. Finally,

marker molecules or drugs can be chemically conjugated onto the phage surface, making it a

versatile imaging or therapy vehicle that may reduce costs and improve life quality

(Steinmetz, 2010). An M13 phage containing cancer cell-targeting motifs on the surface was

chemically modified to conjugate with fluorescent molecules, resulting in both binding and

imaging of human KB cancer cells (Li et al., 2010). Besides being genetically part of the virus,

anti-tumor compounds can also be covalently linked to it. We are currently, by phage

display, selecting phages that adhere and penetrate tumor cells. Following this selection, we

will chemically conjugate anti-cancer compounds (e.g. doxorubicin) to bacteriophages,

equipped with the cancer cell-recognizing peptides on the phage surface. We anticipate that

such a multifunctional nanoparticle, targeted to the tumor using a tumor “homing” peptide,

will enable a significant improvement over existing anti-cancer approaches.

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5.4 Microbial cell factories for the production of drugs On a different perspective, as exemplified in section 4, synthetic biology approaches can be

used for the large scale production of compounds with pharmaceutical applications. One of

the easily employable approaches to develop synthetic pathways is to combine genes from

different organisms, and design a new set of metabolic pathways to produce various natural

and unnatural products. The host organism provides precursors from its own metabolism,

which are subsequently converted to the desired product through the expression of the

heterologous genes (see section 4). Existing examples of synthetic metabolic networks make

use of transcriptional and translational control elements to regulate the expression of

enzymes that synthesize and breakdown metabolites. In these systems, metabolite

concentration acts as an input for other control elements (Andrianantoandro et al., 2006). An

entire metabolic pathway from S. cerevisiae, the mevalonate isoprenoid pathway for

synthesizing isopentyl pyrophosphate, was successfully transplanted into E. coli. In

combination with an inserted synthetic amorpha-4, 11-diene synthase, this pathway

produced large amounts of a precursor to the anti-malarial drug artemisinin. This new

producing strain is very useful since a significant decrease in the drug production time and

costs could be achieved (Martin et al., 2003). In addition to engineering pathways that

produce synthetic metabolites, artificial circuits can be engineered using metabolic

pathways connected to regulatory proteins and transcriptional control elements

(Andrianantoandro et al., 2006). One study describes such a circuit based on controlling

gene expression through acetate metabolism for cell–cell communication (Bulter et al., 2004).

Metabolic networks may embody more complex motifs, such as an oscillatory network. A

recently constructed metabolic network used glycolytic flux to generate oscillations through

the signaling metabolite acetyl phosphate (Fung et al., 2005). The system integrates

transcriptional regulation with metabolism to produce oscillations that are not correlated

with the cell division cycle. The general concerns of constructing transcriptional and protein

interaction-based modules, such as kinetic matching and optimization of reactions for a new

environment, apply for metabolic networks as well. In addition, the appropriate metabolic

precursors must be present. For this purpose, it may be necessary to include other enzymes

or metabolic pathways that synthesize precursors for the metabolite required in a synthetic

network (Leonard et al., 2008; McArthur IV & Fong, 2010).

Many polyketides and nonribosomal peptides are being used as antibiotic, anti-tumor and

immunosuppressant drugs (Neumann & Neumann-Staubitz, 2010). In order to produce

them in heterologous hosts, assembly of all the necessary genes that make up the synthetic

pathways is essential. The metabolic systems for the synthesis of polyketides are composed

of multiple modules, in which an individual module consists of either a polyketide synthase

or a nonribosomal peptide synthetase. Each module has a specific set of catalytic domains,

which ultimately determine the structure of the metabolic product and thus its function.

Recently, Bumpus et al. (2009) presented a proteomic strategy to identify new gene clusters

for the production of polyketides and nonribosomal peptides, and their biosynthetic

pathways, by adapting mass-spectrometry-based proteomics. This approach allowed

identification of genes that are used in the production of the target product in a species, for

which a complete genome sequence is not available. Such newly identified pathways can

then be copied into a new host strain that is more suitable for producing polyketides and

nonribosomal peptides at an industrial scale. This exemplifies that the sources of new

pathways are not limited to species with fully sequenced genomes.

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The use of synthetic biology approaches in the field of metabolic engineering opens enormous possibilities, especially toward the production of new drugs for cancer treatment. Our goal is to design and model a new biosynthetic pathway for the production of natural drugs in E. coli. Key to this is the specification of gene sequences encoding enzymes that catalyze each reaction in the pathway, and whose DNA sequences can be incorporated into devices that lead to functional expression of the molecules of interest (Prather & Martin, 2008). Partial pathways can be recruited from independent sources and co-localized in a single host (Kobayashi et al., 2004). Alternatively, pathways can be constructed for the production of new, non-natural products by engineering existing routes (Martin et al., 2003).

6. Conclusion

Despite all the scientific advances that humankind has seen over the last centuries, there are

still no clear and defined solutions to diagnose and treat cancer. In this sense, the search for

innovative and efficient solutions continues to drive research and investment in this field.

Synthetic biology uses engineering principles to create, in a rational and systematic way,

functional systems based on the molecular machines and regulatory circuits of living

organisms, or to re-design and fabricate existing biological systems. Bioinformatics and

newly developed computational tools play a key role in the improvement of such systems.

Elucidation of disease mechanisms, identification of potential targets and biomarkers,

design of biological elements for recognition and targeting of cancer cells, discovery of new

chemotherapeutics or design of novel drugs and catalysts, are some of the promises of

synthetic biology. Recent achievements are thrilling and promising; yet some of such

innovative solutions are still far from a real application due to technical challenges and

ethical issues. Nevertheless, many scientific efforts are being conducted to overcome these

limitations, and undoubtedly it is expected that synthetic biology together with

sophisticated computational tools, will pave the way to revolutionize the cancer field.

7. References

Alon, U. (2003). Biological networks: the tinkerer as an engineer. Science, Vol.301, No.5641,

(September 2003), pp. 1866-1867, ISSN 0036-8075

Anderson, J.C.; Clarke, E.J.; Arkin, A.P. & Voigt, C.A. (2006). Environmentally controlled

invasion of cancer cells by engineered bacteria. Journal of Molecular Biology, Vol.355,

No.4, (January 2006), pp. 619–627, ISSN 00222836

Andrianantoandro, E.; Basu, S.; Karig, D.K. & Weiss, R. (2006). Synthetic biology: new

engineering rules for an emerging discipline. Molecular Systems Biology Vol.2, No.

2006.0028, (May 2006), pp. 1-14, ISSN 1744-4292

Arkin, A.P. (2001). Synthetic cell biology. Current Opinion in Biotechnology Vol.12, No.6,

(December 2001), pp. 638-644, ISSN 0958-1669

Bansal, V.; Sakizlis, V.; Ross, R.; Perkins, J.D. & Pistikopoulos, E.N. (2003). New algorithms

for mixed-integer dynamic optimization. Computers and Chemical Engineering

Vol.27, No.5, (May 2003), pp. 647-668, ISSN 0098-1354

Bar, H.; Yacoby, I. & Benhar, I. (2008). Killing cancer cells by targeted drug-carrying phage

nanomedicines. BMC Biotechnology Vol.8, No.37, (April 2008), pp. 1-14, ISSN 1472-

6750

www.intechopen.com


Batt, G., Yordanov, B., Weiss, R. & Belta, C. (2007). Robustness analysis and tuning of

synthetic gene networks. Bioinformatics Vol.23, No.18, (July 2007), pp. 2415-2422,

ISSN 1367-4803

Bedi, D.; Musacchio, T.; Fagbohun, O.A.; Gillespie, J.W.; Deinnocentes, P.; Bird, R.C.;

Bookbinder, L.; Torchilin, V.P. & Petrenko, V.A. (2011). Delivery of siRNA into

breast cancer cells via phage fusion protein-targeted liposomes. Nanomedicine:

Nanotechnology, Biology, and Medicine, doi:10.1016/j.nano.2010.10.004, ISSN 1549-

9634

Beisel, C.L. & Smolke, C.D. (2009). Design principles for riboswitch function. PLoS

Computational Biology Vol.5, No.4, (April 2009), e1000363, pp. 1-14, ISSN 1553-734X

Benner, S.A. & Sismour, A.M. (2005). Synthetic biology. Nature Reviews Genetics Vol.6, No.7,

(July 2005), pp. 533–543, ISSN 1471-0056

Brown, K.C. (2010). Peptidic tumor targeting agents: the road from phage display peptide

selections to clinical applications. Current Pharmaceutical Design Vol.16, No.9,

(March 2010), pp. 1040-1054, ISSN 1381-6128

Brustad, E.M. & Arnold, F.H. (2010). Optimizing non-natural protein function with directed

evolution. Current Opinion in Chemical Biology Vol.15, No.2, (April 2010), pp. 1-10,

ISSN 1367-5931

Bulter, T.; Lee, S.G.; Wong, W.W.; Fung, E.; Connor, M.R. & Liao, J.C. (2004). Design of

artificial cell–cell communication using gene and metabolic networks. PNAS

Vol.101, No.8 (February 2004), pp. 2299–2304, ISSN 0027-8424

Bumpus, S.; Evans, B.; Thomas, P.; Ntai, I. & Kelleher, N. (2009). A proteomics approach to

discovering natural products and their biosynthetic pathways. Nature Biotechnology

Vol.27, No.10, (September 2009), pp. 951-956, ISSN 1087-0156

Canton, B.; Labno, A. & Endy, D. (2008). Refinement and standardization of synthetic

biological parts and devices. Nature Biotechnology Vol.26, No. 7, (July 2008), pp. 787-

793, ISSN 1087-0156

Carothers, J.M.; Goler, J.A. & Keasling, J.D. (2009). Chemical synthesis using synthetic

biology. Current Opinion in Biotechnology Vol.20, No.4, (August 2009), pp. 498-503,

ISSN 0958-1669

Chan, L.Y.; Kosuri, S. & Endy, D. (2005). Refactoring bacteriophage T7. Molecular Systems

Biology Vol.1, No. 2005.0018, (September 2005), pp. 1-10, ISSN 1744-4292

Cooling, M.T.; Hunter, P. & Crampin, E.J. (2008). Modelling biological modularity with

CellML. IET Systems Biology Vol.2, No.2, (March 2008), pp. 73-79, ISSN 1751-8849

Crameri, A.; Raillard, S.A.; Bermudez, E. & Stemmer, W.P. (1998). DNA shuffling of a family

of genes from diverse species accelerates directed evolution. Nature Vol.391,

No.6664, (January 1998), pp. 288-291, ISSN 0028-0836

Czar, M.J.; Cai, Y. & Peccoud, J. (2009). Writing DNA with GenoCAD. Nucleic Acids Research

Vol.37, No.2, (May 2009), pp. W40-W47, ISSN 0305-1048

Dasika, M.S. & Maranas, C.D. (2008). OptCircuit: an optimization based method for

computational design of genetic circuits. BMC Systems Biology Vol.2, No.24, pp. 1-

19, ISSN 1752-0509

Datsenko, K.A. & Wanner, B.L. (2000). One-step inactivation of chromosomal genes in

Escherichia coli K-12 using PCR products. PNAS Vol.97, No. 12, (June 2000), pp.

6640-6645, ISSN 0027-8424

www.intechopen.com


Dawid, A.; Cayrol, B. & Isambert, H. (2009). RNA synthetic biology inspired from bacteria:

construction of transcription attenuators under antisense regulation. Physical

Biology Vol.6, No. 025007, (July 2009), pp. 1-10, ISSN 1478-3975

Di Bernardo, D.; Thompson, M.J.; Gardner, T.S.; Chobot, S.E.; Eastwood, E.L.; Wojtovich,

A.P.; Elliott, S.E.; Schaus, S.E. & Collins, J.J. (2005). Chemogenomic profiling on a

genome-wide scale using reverse-engineered gene networks. Nature Biotechnology

Vol.23, No.3, (March 2005), pp. 377-383, ISSN 1087-0156

Di Ventura, B.; Lemerle, C.; Michalodimitrakis, K. & Serrano, L. (2006). From in vivo to in

silico biology and back. Nature Vol.443, No.7111, (October 2006), pp. 527-533, ISSN

0028-0836

Dougherty, M.J. & Arnold, F.H. (2009). Directed evolution: new parts and optimized

function. Current Opinion in Biotechnology Vol.20, No.4, (August 2009), pp. 486-491,

ISSN 0958-1669

Du B, Han H, Wang Z, Kuang L, Wang L, Yu L, Wu M, Zhou Z, Qian M. (2010). Targeted

drug delivery to hepatocarcinoma in vivo by phage-displayed specific binding

peptide. Molecular Cancer Research Vol.8, No.2, (February 2010), pp.135-144, ISSN

1541-7786

Ellis, T.; Wang, X. & Collins, J.J. (2009). Diversity-based, model guided construction of

synthetic gene networks with predicted functions. Nature Biotechnology Vol.27,

No.5, (May 2009), pp. 465– 471, ISSN 1087-0156

Elowitz, M.B. & Leibler, S. (2000). A synthetic oscillatory network of transcriptional

regulators. Nature Vol.403, No.6767, (January 2000), pp. 335-338, ISSN 0028-0836

Elowitz, M.B.; Levine, A.J.; Siggia, E.D. & Swain, P.S. (2002). Stochastic gene expression in a

single cell. Science Vol.297, No.5584, (August 2002), pp. 1183–1186, ISSN 0036-8075

Endy, D. (2005). Foundations for engineering biology. Nature Vol.438, No. 7067, (November

2005), pp. 449-453, ISSN 0028-0836

Engler, C.; Gruetzner, R.; Kandzia, R. & Marillonnet, S. (2009). Golden gate shuffling: a one

pot DNA shuffling method based on type ils restriction enzymes. PLoS ONE Vol.4,

No.5, (May 2009), e5553, pp. 1-9, ISSN 1932-6203

Eriksson, F.; Tsagozis, P.; Lundberg, K.; Parsa, R.; Mangsbo, S.M.; Persson, M.A.; Harris,

R.A. & Pisa, P.J. (2009). Tumor-specific bacteriophages induce tumor destruction

through activation of tumor-associated macrophages. The Journal of Immunology


Ferreira, C.S.; Matthews, C.S. & Missailidis, S. (2006). DNA aptamers that bind to MUC1

tumour marker: design and characterization of MUC1-binding single-stranded

DNA aptamers. Tumour Biology Vol.27, No.6, (October 2006), pp. 289-301, ISSN

1010-4283

Fisher, J. & Henzinger, T.A. (2007). Executable cell biology. Nature Biotechnology Vol.25,

No.11, (November 2007), pp. 1239–1249, ISSN 1087-0156

Forbes, N.S. (2010). Engineering the perfect (bacterial) cancer therapy. Nature Reviews Cancer

Vol.10, No.11, (October 2010), pp. 785-794, ISSN 1474-175X

Francois, P. & Hakim, V. (2004). Design of genetic networks with specified functions by

evolution in silico. PNAS Vol.101, No.2, (January 2004), pp. 580–585, ISSN 0027-

8424

www.intechopen.com


Funahashi, A.; Morohashi, M. & Kitano, H. (2003). CellDesigner: a process diagram editor

for gene-regulatory and biochemical networks. BIOSILICO Vol.1, No.5, (November

2003),pp. 159–162, ISSN 1478-5382

Fung, E.; Wong, W.W.; Suen, J.K.; Bulter, T.; Lee, S.G. & Liao, J.C. (2005). A synthetic gene

metabolic oscillator. Nature Vol.435, No.7038, (May 2005), pp. 118–122, ISSN 0028-

0836

Gao, C.; Mao, S.; Ronca, F.; Zhuang, S.; Quaranta, V.; Wirsching, P. & Janda, K.D. (2003). De

novo identification of tumor-specific internalizing human antibody-receptor pairs

by phage-display methods. Journal of Immunological Methods. Vol.274, No.1-2,

(March 2003), pp. 185-197, ISSN 0022-1759

Gardner, T.S.; Cantor, C.R. & Collins, J.J. (2000). Construction of a genetic toggle switch in

Escherichia coli. Nature Vol.403, No.6767, (January 2000), pp. 339-342, ISSN 0028-

0836

Garst, A.D. & Batey, R.T. (2009). A switch in time: detailing the life of a riboswitch.

Biochimica Biophysica Acta Vol.1789, No.9-10, (September-October 2009), pp. 584-

591, ISSN 0006-3002

Goler, J.A. (2004). A design and simulation tool for synthetic biological systems. Cambridge,

MA: MIT

Golynskiy, M.V. & Seelig, B. (2010). De novo enzymes: from computational design to mRNA

display. Trends in Biotechnology Vol.28, No.7, (July 2010), pp. 340-345, ISSN 0167-

7799

Grunberg, R.; Nilges, M. & Leckner, J. (2007). Biskit —a software platform for structural

bioinformatics. Bioinformatics Vol.23, No.6, (March 2007), pp. 769-770, ISSN 1367-

4803

Ham, T.S.; Lee, S.K.; Keasling, J.D. & Arkin, A.P. (2006). A tightly regulated inducible

expression system utilizing the fim inversion recombination switch. Biotechnology

and Bioengineering Vol.94, No.1, (May 2006), pp. 1–4, ISSN 0006-3592

Hartley, J.L. (2003). Use of the gateway system for protein expression in multiple hosts.

Current Protocols in Protein Science Chap5: Unit 5.17

Hawkins, T.; Luban, S. & Kihara, D. (2006). Enhanced automated function prediction using

distantly related sequences and contextual association by PFP. Protein Science

Vol.15, No.6, (June 2006), pp. 1550-1556, ISSN 1469-896X

Hianik, T. & Wang, J. (2009). Electrochemical aptasensors – recent achievements and

perspectives. Electroanalysis Vol.21, No.11, (June 2009), pp. 1223-1235, ISSN 1521-

4109

Hill, A.D.; Tomshine, J.R.; Weeding, E.M.; Sotiropoulos, V. & Kaznessis, Y.N. (2008).

SynBioSS: the synthetic biology modeling suite. Bioinformatics Vol.24, No.21,

(November 2008),pp. 2551-2553, ISSN 1367-4803

Hofacker, I.L. (2003). Vienna RNA secondary structure server. Nucleic Acids Research Vol.31,

No.13, (July 2003), pp. 3429-3431, ISSN 0305-1048

Isaacs, F.J.; Dwyer, D.J. & Collins, J.J. (2006). RNA synthetic biology. Nature Biotechnology

Vol.24, No.5, (May 2006), pp. 545-554, ISSN 1087-0156

Isalan, M.; Lemerle, C.; Michalodimitrakis, K.; Horn, C.; Beltrao, P.; Raineri, E.; Garriga-

Canut, M.; Serrano, L. (2008). Evolvability and hierarchy in rewired bacterial gene

networks. Nature Vol.452, No.7189, (April 2008), pp. 840–845, ISSN 0028-0836

www.intechopen.com


Jemal, A.; Bray, F.; Center, M.M.; Ferlay, J.; Ward, E. & Forman, D. (2011). Global cancer

statistics. CA Cancer Journal for Clinicians Vol.61, No.2, (March-April 2011),pp. 69-

90, ISSN 0007-9235

Kaiser, J. (2005). Gene therapy. Putting the fingers on gene repair. Science Vol.310, No.5756,

(December 2005), pp.1894-1896, ISSN 0036-8075

Kaznessis, Y.N. (2009). Computational methods in synthetic biology. Biotechnology Journal

Vol.4, No.10, (October 2009), pp.1392-1405, ISSN 1860-7314

Kelly, J.; Rubin, A.J.; Davis, J. II; Ajo-Franklin, C.M.; Cumbers, J.; Czar, M.J.; de Mora, K.;

Glieberman, A.I.; Monie, D.D. & Endy, D. (2009). Measuring the activity of BioBrick

promoters using an in vivo reference standard. Journal of Biological Engineering


Kirkpatrick, S.; Gelatt, C.D. Jr. & Vecchi, M.P. (1983). Optimization by Simulated Annealing.

Science Vol.220, No.4598, (May 1983), pp. 671-680, ISSN 0036-8075

Kitano, H. (2002). Systems biology: a brief overview. Science Vol.295, No.5560, (March 2002),

pp. 1662-1664, ISSN 0036-8075

Kizer, L.; Pitera, D.J.; Pfleger, B.F. & Keasling, J.D. (2008). Application of functional

genomics to pathway optimization for increased isoprenoid production. Applied

and Environmental Microbiology Vol.74, No.10, (May 2008), pp. 3229–3241, ISSN

0099-2240

Kobayashi, H.; Kaern, M.; Araki, M.; Chung, K.; Gardner, T.S.; Cantor, C.R. & Collins, J.J.

(2004). Programmable cells: interfacing natural and engineered gene networks.

PNAS Vol.101, No.22, (June 2004), pp. 8414–8419, ISSN 0027-8424

Koide, T.; Pang, W.L. & Baliga, N.S. (2009). The role of predictive modeling in rationally

reengineering biological systems. Nature Reviews Microbiology Vol.7, No.4, (April

2009), pp. 297-305, ISSN 1740-1526

Kosuri, S.; Kelly, J.R. & Endy, D. (2007). TABASCO: a single molecule, base pair resolved

gene expression simulator. BMC Bioinformatics Vol.8, No.480, (December 2007), pp.

1-15, ISSN 1471-2105

Lartigue, C.; Glass, J.I.; Alperovich, N.; Pieper, R.; Parmar, P.P.; Hutchison III, C.A.; Smith,

H.O. & Venter, J.C. (2007). Genome transplantation in bacteria: changing one

species to another. Science Vol.317, No.5838, (August 2007), pp. 632-638, ISSN 0036-

8075

Leonard, E.; Nielsen, D.; Solomon, K. & Prather, K.J. (2008). Engineering microbes with

synthetic biology frameworks. Trends in Biotechnology Vol.26, No.12, (December

2008), pp. 674-681, ISSN 0167-7799

Li K, Chen Y, Li S, Nguyen HG, Niu Z, You S, Mello CM, Lu X, Wang Q. (2010). Chemical

modification of M13 bacteriophage and its application in cancer cell imaging.

Bioconjugate Chemistry Vol.21, No.7, (December 2010), pp. 1369-1377, ISSN 1043-

1802

Loettgers, A. (2007). Model organisms and mathematical and synthetic models to explore

regulation mechanisms. Biological Theory Vol.2, No.2, (December 2007), pp. 134-142,

ISSN 1555-5542

Mandell, J.G. & Barbas, C.F. III (2006). Zinc Finger Tools: custom DNA binding domains for

transcription factors and nucleases. Nucleic Acids Research Vol.34, (July 2006), pp.

W516-523, ISSN 0305-1048

www.intechopen.com


Marchisio, M.A. & Stelling, J. (2008). Computational design of synthetic gene circuits with

composable parts. Bioinformatics Vol.24, No.17, (September 2008), pp.1903–1910,

ISSN 1367-4803

Marchisio, M.A. & Stelling, J. (2009). Computational design tools for synthetic biology.

Current Opinion in Biotechnology Vol.20, No.4, (August 2009), pp. 479-485, ISSN

0958-1669

Marguet, P.; Balagadde, F.; Tan, C. & You, L. (2007). Biology by design: reduction and

synthesis of cellular components and behavior. Journal of the Royal Society Interface

Vol.4, No.15, (August 2007), pp. 607-623, ISSN 1742-5689

Markham, N.R. & Zuker, M. (2008). UNAFold: software for nucleic acid folding and

hybridization. Methods Molecular Biology Vol.453, No.I, pp. 3-31, ISSN 1064-3745

Martin, V.J.; Pitera, D.J.; Withers, S.T.; Newman, J.D. & Keasling, J.D. (2003). Engineering a

mevalonate pathway in Escherichia coli for production of terpenoids. Nature

Biotechnology Vol.21, No.7, (July 2003), pp.796–802, ISSN 1087-0156

Matsuoka, Y.; Ghosh, S. & Kitano, H. (2009). Consistent design schematics for

biological systems: standardization of representation in biological engineering.

Journal of the Royal Society Interface Vol.6, No.4, (August 2009), pp. S393-S404, ISSN

1742-5689

McArthur IV, G.H. & Fong, S.S. (2010). Toward engineering synthetic microbial metabolism.

Journal of Biomedicine and Biotechnology doi:10.1155/2010/459760, ISSN 1110-7243

Mirschel, S.; Steinmetz, K.; Rempel, M.; Ginkel, M. & Gilles, E.D. (2009). PROMOT: modular

modeling for systems biology. Bioinformatics Vol.25, No.5, (March 2009), pp. 687-

689, ISSN 1367-4803

Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S. & Olson,

A.J. (2009). AutoDock4 and AutoDockTools4: automated docking with selective

receptor flexibility. Journal of Computational Chemistry Vol.30, No.16, (December

2009), pp. 2785-2791, ISSN 0192-8651

Mueller, S.; Coleman, J.R. & Wimmer, E. (2009). Putting synthesis into biology: a viral view

of genetic engineering through de novo gene and genome synthesis. Chemistry &

Biology Vol.16, No.3, (March 2009), pp. 337-347, ISSN 1074-5521

Neumann, H. & Neumann-Staubitz, P. (2010). Synthetic biology approaches in drug

discovery and pharmaceutical biotechnology. Applied Microbiology and Biotechnology

Vol.87, No.1, (June 2010), pp. 75-86, ISSN 0175-7598

Nielsen, J. (2001). Metabolic engineering. Applied Microbiology and Biotechnology Vol.55, No.3,

(April 2001), pp. 263-283, ISSN 0175-7598

Park, J.H.; Lee, S.Y.; Kim, T.Y. & Kim, H.U. (2008). Application of systems biology for

bioprocess development. Trends in Biotechnology Vol.26, No.8, (August 2008), pp.

404–412, ISSN 0167-7799

Pawelek, J.; Low, K. & Bermudes, D. (2003). Bacteria as tumour-targeting vectors. Lancet

Oncology Vol.4, No.9, (September 2003), pp. 548–556, ISSN 1470-2045

Pedersen, M. & Phillips, A. (2009). Towards programming languages for genetic engineering

of living cells. Journal of the Royal Society Interface Vol.6, No.4, (August 2009), pp.

S437-S450, ISSN 1742-5689

www.intechopen.com


Prather, K. & Martin, C.H. (2008). De novo biosynthetic pathways: rational design of

microbial chemical factories. Current Opinion in Biotechnology Vol.19, No.5, (October

2008), pp. 468–474, ISSN 0958-1669

Price, N.D. & Shmulevich, I. (2007). Biochemical and statistical network models for systems

biology. Current Opinion in Biotechnology Vol.18, No.4, (August 2007), pp. 365–370,

ISSN 0958-1669

Purnick, P.E. & Weiss, R. (2009). The second wave of synthetic biology: from modules to

systems. Nature Reviews Molecular Cell Biology Vol.10, No.6, (June 2009), pp. 410-422,

ISSN 1471-0072

Quan, J. & Tian, J. (2009). Circular polymerase extension cloning of complex 1 gene libraries

and pathways. PLoS ONE Vol.4, No.7, (July 2009), e6441, pp.1-6, ISSN 1932-6203

Richardson, S.M.; Wheelan, S.J.; Yarrington, R.M. & Boeke, J.D. (2006). GeneDesign: rapid,

automated design of multikilobase synthetic genes. Genome Research Vol.16, No.4,

(April 2006), pp. 550-556, ISSN 1088-9051

Ritchie, D.W. (2008). Recent progress and future directions in protein–protein

docking.Current Protein & Peptide Science Vol.9, No.1, (February 2008), pp. 1-15,

ISSN 1389-2037

Ro, D.K.; Paradise, E.M.; Ouellet, M.; Fisher, K.J.; Newman, K.L.; Ndungu, J.M.; Ho, K.A.;

Eachus, R.A.; Ham, T.S.; Kirby, J.; Chang, M.C.; Withers, S.T.; Shiba, Y.; Sarpong, R.

& Keasling, J.D. (2006). Production of the antimalarial drug precursor artemisinic

acid in engineered yeast. Nature Vol.440, No.7086, (April 2006), pp. 940–943, ISSN

0028-0836

Rocha, I.; Forster, J. & Nielsen, J. (2008). Design and application of genome-scale

reconstructed metabolic models. Methods in Molecular Biology Vol.416, No.IIIB, pp.

409-431, ISSN 1064-3745

Rocha, I.; Maia, P.; Evangelista, P.; Vilaça, P.; Soares, S.; Pinto, J.P.; Nielsen, J.; Patil, K.R.;

Ferreira, E.C. & Rocha, M. (2010). OptFlux: an open-source software platform for in

silico metabolic engineering. BMC Systems Biology Vol.4, No. 45, pp. 1-12, ISSN

1752-0509

Rodrigo, G.; Carrera, J. & Jaramillo, A. (2007a). Asmparts: assembly of biological model

parts. Systems and Synthetic Biology Vol.1, No.4, (December 2007), pp. 167-170, ISSN

1872-5325

Rodrigo, G.; Carrera, J. & Jaramillo, A. (2007b). Genetdes: automatic design of

transcriptional networks. Bioinformatics Vol.23, No.14, (July 2007), pp.1857-1858,

ISSN 1367-4803

Rodrigues, L.R.; Teixeira, J.A.; Schmitt, F.; Paulsson, M. & Lindmark Måsson, H. (2007). The

role of osteopontin in tumour progression and metastasis in breast cancer. Cancer

Epidemology Biomarkers & Prevention Vol.16, No.6, (June 2007), pp. 1087–1097, ISSN

1055-9965

Saito, H. & Inoue, T. (2009). Synthetic biology with RNA motifs. The International Journal of

Biochemistry & Cell Biology Vol.41, No.2, (February 2009), pp. 398-404, ISSN 1357-

2725

Salis, H.M., Mirsky, E.A. & Voigt, C.A. (2009). Automated design of synthetic ribosome

binding sites to control protein expression. Nature Biotechnology Vol.27, No.10,

(October 2009), pp. 946–950, ISSN 1087-0156

www.intechopen.com


Segre, D.; Vitkup, D. & Church, G.M. (2002). Analysis of optimality in natural and perturbed

metabolic networks. PNAS Vol.99, No.23, (November 2001), pp. 15112-15117, ISSN

0027-8424

Shetty, R.P.; Endy, D. & Knight, T.F. Jr. (2008). Engineering BioBrick vectors from BioBrick

parts. Journal of Biological Engineering Vol.2, No.1, (April 2008), pp.5-17 ISSN 1754-

1611

Simons, K.T.; Bonneau, R.; Ruczinski, I. & Baker, D. (1999). Ab initio protein structure

prediction of CASP III targets using ROSETTA. Proteins Vol.3, pp. 171-176, ISSN

0887-3585

Steinmetz, N.F. (2010).Viral nanoparticles as platforms for next-generation therapeutics and

imaging devices. Nanomedicine: Nanotechnology, Biology, and Medicine Vol.6, No.5,

(October 2010), pp. 634-641, ISSN 1549-9634

Stoltenburg, R.; Reinemann, C. & Strehlitz, B. (2007). SELEX—A (r)evolutionary method to

generate high-affinity nucleic acid ligands. Biomolecular Engineering Vol.24, No.4,

(October 2007), pp. 381–403, ISSN 1389-0344

Topp, S. & Gallivan, J.P. (2007). Guiding bacteria with small molecules and RNA. Journal of

the American Chemical Society Vol.129, No.21, (May 2007), pp. 6807-6811, ISSN 0002-

7863

Tyo, K.E.; Alper, H.S. & Stephanopoulos, G. (2007). Expanding the metabolic engineering

toolbox: more options to engineer cells. Trends in Biotechnology Vol.25, No.3, (March

2007), pp. 132-137, ISSN 0167-7799

Tyo, K.E.J.; Ajikumar, P.K. & Stephanopoulos, G. (2009). Stabilized gene duplication enables

long-term selection-free heterologous pathway expression. Nature Biotechnology

Vol.27, No.8, (August 2009), pp. 760–765, ISSN 1087-0156

Villalobos, A.; Ness, J.E.; Gustafsson, C.; Minshull, J. & Govindarajan, S. (2006). Gene

Designer: a synthetic biology tool for constructing artificial DNA segments. BMC

Bioinformatics Vol.7, No.285, (June 2006), pp.1-8, ISSN 1471-2105

Voloshchuk, N. & Montclare, J.K. (2010). Incorporation of unnatural amino acids for

synthetic biology. Molecular Biosystems Vol.6, No.1, (January 2010), pp. 65-80, ISSN

1742-2051

Wang, H.H.; Isaacs, F.J.; Carr, P.A.; Sun, Z.Z.; Xu, G.; Forest, C.R. & Church, G.M. (2009).

Programming cells by multiplex genome engineering and accelerated evolution.

Nature Vol.460, No.7257, (August 2009), pp. 894–898, ISSN 0028-0836

Wierling, C.; Herwig, R. & Lehrach, H. (2007). Resources, standards and tools for systems

biology. Briefings in Functional Genomics and Proteomics Vol.6, No.3, (September

2007), pp. 240-251, ISSN 1473-9550

Win, M.N. & Smolke, C.D. (2007). From the cover: a modular and extensible RNA-based

gene-regulatory platform for engineering cellular function. PNAS Vol.104, No.36,

(September 2007), pp. 14283–14288, ISSN 0027-8424

Winkler, W.C. & Breaker, R.R. (2005). Regulation of bacterial gene expression by

riboswitches. Annual Review of Microbiology Vol.59, pp. 487-517, ISSN 0066-4227

Xu, J.; Li, M.; Kim, D. & Xu, Y. (2003). RAPTOR: optimal protein threading by linear

programming. Journal of Bioinformatics and Computational Biology Vol.1, No.1, (April

2003), pp. 95-117, ISSN 0219-7200

www.intechopen.com


Yokobayashi, Y.; Weiss, R. & Arnold, F.H. (2002). Directed evolution of a genetic circuit.

PNAS Vol.99, No.26, (September 2002), pp. 16587–16591, ISSN 0027-8424

You, L. (2004). Toward computational systems biology. Cell Biochemistry and Biophysics

Vol.40, No.2, pp. 167–184, ISSN 1085-9195

www.intechopen.com

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Nowadays it is difficult to imagine an area of knowledge that can continue developing without the use ofcomputers and informatics. It is not different with biology, that has seen an unpredictable growth in recentdecades, with the rise of a new discipline, bioinformatics, bringing together molecular biology, biotechnologyand information technology. More recently, the development of high throughput techniques, such asmicroarray, mass spectrometry and DNA sequencing, has increased the need of computational support tocollect, store, retrieve, analyze, and correlate huge data sets of complex information. On the other hand, thegrowth of the computational power for processing and storage has also increased the necessity for deeperknowledge in the field. The development of bioinformatics has allowed now the emergence of systems biology,the study of the interactions between the components of a biological system, and how these interactions giverise to the function and behavior of a living being. This book presents some theoretical issues, reviews, and avariety of bioinformatics applications. For better understanding, the chapters were grouped in two parts. InPart I, the chapters are more oriented towards literature review and theoretical issues. Part II consists ofapplication-oriented chapters that report case studies in which a specific biological problem is treated withbioinformatics tools.

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